Nondiagonal Mixture of Dirichlet Network Distributions for Analyzing a Stock Ownership Network

Abstract

Block modeling is widely used in studies on complex networks. The cornerstone model is the stochastic block model (SBM), widely used over the past decades. However, the SBM is limited in analyzing complex networks as the model is, in essence, a random graph model that cannot reproduce the basic properties of many complex networks, such as sparsity and heavy-tailed degree distribution. In this paper, we provide an edge exchangeable block model that incorporates such basic features and simultaneously infers the latent block structure of a given complex network. Our model is a Bayesian nonparametric model that flexibly estimates the number of blocks and takes into account the possibility of unseen nodes. Using one synthetic dataset and one real-world stock ownership dataset, we show that our model outperforms state-of-the-art SBMs for held-out link prediction tasks.

References in corpus (7)

• Emergence of scaling in random networks
• Stochastic blockmodels and community structure in networks
• Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network
• Evaluating Link Prediction Methods
• Nonparametric Bayesian inference of the microcanonical stochastic block model
• Sparse graphs using exchangeable random measures
• Nonparametric weighted stochastic block models